A method, device, equipment and medium for monitoring diseases of a steel-concrete joint section

Abstract

The application provides a steel-concrete joint segment disease monitoring method, device, equipment and medium, and relates to the technical field of bridge engineering. The method comprises the following steps: obtaining reflection signal data of the steel-concrete joint segment through electromagnetic detection, and constructing a slice two-dimensional image of the internal structure of the steel-concrete joint segment according to the reflection signal data; adopting a pre-trained model to perform target recognition segmentation on the slice two-dimensional image to obtain a monitoring target image; respectively processing different monitoring target images to obtain displacement monitoring information, steel bar slip and deformation information and void area volume information, so as to obtain steel-concrete joint segment disease information. The application obtains internal structure information of the steel-concrete joint segment through electromagnetic detection, superimposes one-dimensional signals to generate a bidirectional two-dimensional profile graph, detects a target through the two-dimensional profile graph and analyzes the target, so as to continuously monitor whether the steel-concrete joint surface is separated, whether there is a void and whether there is interface slip.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge engineering, in particular to a steel-concrete joint segment disease monitoring method, device, equipment and medium. BACKGROUND

[0002] The composite structure technology plays an important role in improving the stress performance of the main girder and the tower. The hybrid girder joint segment of the cable-stayed bridge is to realize the structural transition and load transfer between the steel girder and the concrete girder by using the composite structure technology. The steel-concrete joint segment combines the advantages of the steel box girder and the concrete box girder, uses the characteristics of the relatively small self-weight and high strength of the steel box girder to increase the span of the bridge, and uses the characteristics of the large self-weight of the concrete to balance the dead load and live load of the main span. This combination not only improves the stiffness and strength of the bridge structure, but also saves materials and reduces the engineering cost, and has high technical value and economic benefits.

[0003] However, the steel-concrete joint segment has a complex structure, is prone to stress concentration and stiffness mutation, and the ordinary concrete has a large shrinkage in the later period and a low bonding strength with the steel plate, which is prone to problems such as interlayer slip, loose through reinforcement, and interlayer void, seriously affecting the safety of the bridge. Therefore, the steel-concrete joint segment as the key part of the hybrid girder structure needs to be monitored in real time. SUMMARY

[0004] The purpose of the present application is to provide a steel-concrete joint segment disease monitoring method, device, equipment and medium to improve the above problems. In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0005] In a first aspect, the present application provides a steel-concrete joint segment disease monitoring method, comprising:

[0006] acquiring reflection signal data of the steel-concrete joint segment by electromagnetic detection, and constructing a slice two-dimensional image of the internal structure of the steel-concrete joint segment according to the reflection signal data;

[0007] performing target recognition segmentation on the slice two-dimensional image by using a pre-trained model to obtain a monitoring target image, the monitoring target image comprising a steel bar reflection image, a steel-concrete contact surface image and a void image;

[0008] performing ellipse fitting on the steel bar reflection image to obtain fitting points of each steel bar, and obtaining steel bar slip and deformation information according to the position change of the fitting points of the steel bar;

[0009] performing edge extraction on the steel-concrete contact surface image, and drawing a maximum inscribed circle between the extracted steel plate edge and the concrete edge, and judging whether there is an interface separation between the steel plate and the concrete according to the size of the maximum inscribed circle to obtain interface information;

[0010] construct a three-dimensional model of the void area based on the void image, and obtain void area volume information;

[0011] obtain steel-concrete joint segment disease information based on the interface information, steel bar slip and deformation information, and void area volume information.

[0012] In a second aspect, the present application provides a steel-concrete joint segment disease monitoring device, comprising:

[0013] A first construction module is configured to acquire reflection signal data of the steel-concrete joint segment through electromagnetic detection, and construct a slice two-dimensional image of the internal structure of the steel-concrete joint segment according to the reflection signal data.

[0014] A first segmentation module is configured to perform target recognition segmentation on the slice two-dimensional image by using a pre-trained model, and obtain a monitoring target image, wherein the monitoring target image comprises a steel bar reflection image, a steel-concrete contact surface image, and a void image.

[0015] A first processing module is configured to perform ellipse fitting on the steel bar reflection image, and obtain fitting points of each steel bar, and obtain steel bar slip and deformation information according to the position change of the fitting points of the steel bar.

[0016] A second processing module is configured to perform edge extraction on the steel-concrete contact surface image, draw a maximum inscribed circle between the extracted steel plate edge and the concrete edge, and determine whether there is an interface separation between the steel plate and the concrete according to the size of the maximum inscribed circle, and obtain interface information.

[0017] A third processing module is configured to construct a three-dimensional model of the void area based on the void image, and obtain void area volume information.

[0018] A fourth processing module is configured to obtain steel-concrete joint segment disease information based on the interface information, steel bar slip and deformation information, and void area volume information.

[0019] In a third aspect, the present application further provides a steel-concrete joint segment disease monitoring device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the steel-concrete joint segment disease monitoring method described above when executing the computer program.

[0020] In a fourth aspect, the present application further provides a readable storage medium, wherein the readable storage medium stores a computer program, and the computer program is executable on a processor to implement the steps of the steel-concrete joint segment disease monitoring method described above.

[0021] The present application has the following beneficial effects:

[0022] The application obtains internal structure information of the steel-concrete joint section through electromagnetic detection, superimposes one-dimensional signals and converts to generate a two-dimensional profile, detects and analyzes the target through the two-dimensional profile, so that whether the steel-concrete joint surface is separated, whether there is a void, and whether there is interface slip can be continuously monitored. The application realizes non-destructive through detection of the internal structure of the steel-concrete joint section, can evaluate the service performance and predict the service life of the structure through accumulated time series data, and provides guidance for maintenance decision.

[0023] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent from the description, or can be learned by practice of the present application according to the embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0025] Figure 1 The flow chart of the steel-concrete joint section disease monitoring method of the embodiments of the present application;

[0026] Figure 2 The schematic diagram of the ellipse fitting of the embodiments of the present application;

[0027] Figure 3 The schematic diagram of the steel-concrete joint section disease monitoring device of the embodiments of the present application;

[0028] Figure 4 The schematic diagram of the steel-concrete joint section disease monitoring device of the embodiments of the present application;

[0029] In the figure, 100 is a first construction module, 200 is a first segmentation module, 300 is a first processing module, 310 is a fitting unit, 320 is a first calculation unit, 330 is a first processing unit, 340 is a second calculation unit, 350 is a second processing unit, 400 is a second processing module, 410 is an extraction unit, 420 is a generation unit, 430 is a search unit, 440 is a judgment unit, 500 is a third processing module, 510 is an identification unit, 520 is a third calculation unit, 530 is a fourth calculation unit, 540 is a fifth calculation unit, 600 is a fourth processing module, 800 is a steel-concrete joint section disease monitoring device, 801 is a processor, 802 is a memory, 803 is a multimedia assembly, 804 is an I / O interface, and 805 is a communication assembly. DETAILED DESCRIPTION

[0030] In order to make the purposes, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0031] It should be noted that similar reference numerals and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings.

[0032] The existing steel-concrete joint disease detection means is mainly contact detection. The monitoring of interlayer interface displacement is mainly through the arrangement of strain gauges, resistance gauges, and other sensing devices on the surface at different positions, processing the signals received by different sensors to determine whether there is interface slip, and further calculating the size of the interface slip. Or pre-embedded distributed optical fiber sensors are used for sensing, which need to be laid and placed in advance during the construction stage, which is not suitable for engineering structures that are not pre-embedded in advance, and the cost is high. The abnormal detection of steel-concrete joint section void, steel-concrete contact surface separation, etc. is detected by pressure gauges, displacement meters, etc. acting on the surface, which can only qualitatively determine whether there is void, and cannot realize quantitative void measurement. There is also a method of using acoustic vibration for detection, that is, through the echo signal to detect void, etc. The above conventional detection methods can only qualitatively determine whether there is void, and cannot effectively measure the void volume and the degree of interlayer separation. At the same time, the internal structure of the steel-concrete joint section is complex, and the traditional measurement method cannot effectively identify multiple internal targets.

[0033] Embodiment 1

[0034] Reference Figure 1 To solve the existing technical problems, the present application provides a steel-concrete joint section disease monitoring method, comprising steps S100, S200, S300, S400, S500 and S600;

[0035] Step S100, acquiring reflection signal data of the steel-concrete joint section through electromagnetic detection, constructing a slice two-dimensional image of the internal structure of the steel-concrete joint section according to the reflection signal data, specifically:

[0036] The electromagnetic signal transmitting device is multi-array and multi-angle, and is equipped with a synchronous reflection signal receiving device.

[0037] The higher the frequency of the signal transmitting device, the shallower the detection depth and the higher the resolution. The vertical resolution is:

[0038]

[0039] Wherein, Δr is the vertical resolution of the electromagnetic wave signal, V is the propagation speed of the electromagnetic wave in the medium, f c is the center frequency of the transmitted signal.

[0040] The lateral resolution is:

[0041]

[0042] Wherein, Δl is the lateral resolution of the electromagnetic wave detecting object, d is the depth of the object, V is the propagation speed of the electromagnetic wave in the medium, f c is the center frequency of the transmitted signal.

[0043] In order to ensure that the through detection detects all conditions in the steel-concrete joint section, the longitudinal resolution required for monitoring is determined by the thickness of the steel plate and the diameter of the steel bar in the steel-concrete joint section. The longitudinal resolution is required to be not less than the minimum value of the thickness of the steel plate or the diameter of the steel bar. When calculating the lateral resolution, d is required to be the maximum depth of the steel-concrete joint section, and the lateral resolution is selected according to the minimum distance between adjacent steel bars.

[0044] According to the above principle, appropriate transmitting power and frequency are selected according to the structure size to realize the separation of the steel plate and the concrete interface, the separation of the steel bar in the transverse and longitudinal directions, and the through detection of the joint section.

[0045] The signal transmitter transmits high-frequency electromagnetic waves, the reflection signal receiving device (receiving antenna) receives the reflected waves, records the time difference and amplitude between the transmission and reception, and obtains the reflection signal data. The reflection signal data is processed through filtering, gain, signal superposition, offset correction and other operations. The time domain data is converted into depth domain data, and the collected data is plotted into a two-dimensional profile according to the position of the survey line to construct a two-dimensional image of the slice of the steel-concrete joint section.

[0046] The two-dimensional slice image is the image of several sections of the steel-concrete joint section, including the transverse slice image and the longitudinal slice image. The transverse slice image is parallel to the bridge deck, and the longitudinal slice image is perpendicular to the driving lane.

[0047] S200, a pre-trained model is used to perform target recognition and segmentation on the two-dimensional slice image to obtain a monitoring target image, the monitoring target image including a steel bar reflection image, a steel-concrete contact surface image and a void image;

[0048] The steel-concrete joint section is composed of different materials such as steel plate, steel bar, cast concrete, and air, which will present different morphological characteristics on the two-dimensional image. First, a large number of two-dimensional section images of the steel-concrete joint section are collected and labeled to construct a database of steel bar reflection images, steel-concrete contact surface images, and void images. Based on the Faster-RCNN model, target recognition training is performed. The trained model can identify the steel-concrete (steel plate and concrete) contact surface, steel bar, and void in the image, and obtain the two-dimensional coordinate position of the target in the image. According to the two-dimensional coordinate position, segmentation is performed to obtain the image of each target, and each target is numbered. The numbering rule is to label the target center coordinates. This numbering can reflect the actual position corresponding to the image, which is convenient for subsequent calculation of the actual depth and position of the disease part.

[0049] This step uses a target detection algorithm to intelligently detect the target of interest in the image, realizes target classification and screening, removes ambiguous information interference, and accurately obtains various targets and spatial positions.

[0050] S300, ellipse fitting is performed on the steel bar reflection image to obtain the fitting point of each steel bar, and the steel bar slip and deformation information are obtained according to the position change of the fitting point of the steel bar;

[0051] First, the steel bar image is recognized and segmented, and the fitting curve is obtained by ellipse fitting. The general ellipse fitting equation of the fitting curve is:

[0052] Ax 2 +Bxy+Cy 2 +Dx+Ey+F=0

[0053] Where (x, y) is the coordinate of a point on the ellipse, and A, B, C, D, E, and F are constants. By taking the derivative of x and limiting the slope of the ellipse to 0 (limiting the long axis or short axis of the ellipse to be parallel to the coordinate axis), we get:

[0054]

[0055] Substitute x into the general ellipse equation, i.e.:

[0056]

[0057] Solve for the coordinates of x and y to get two sets of coordinate values (the upper and lower vertices of the ellipse). Take the upper vertex of the ellipse as the fitting point of the steel bar, as shown in Figure 2 , which represents the coordinate position of the steel bar in the two-dimensional section image. Number the fitting points of each steel bar, with the fitting point coordinates as the basis.

[0058] Calculate steel bar slip: calculate the coordinate change value of the fitting points of the same numbered steel bar in the adjacent steel bar reflection image; the adjacent steel bar reflection image is the steel bar reflection image adjacent in time; thus the change of the steel bar position with time can be obtained, so as to analyze the slip amount and slip direction of the steel bar;

[0059] According to the coordinate change value of the fitting points of the steel bar, the steel bar slip information is obtained, for example, if the coordinate change value of the fitting points compared with the initial coordinate exceeds the preset threshold value, it is judged that the interface slip occurs, that is, the bonding between the steel bar and the concrete layer is not firm, and there is relative rolling problem.

[0060] It should be noted that the three-dimensional structure of the steel bar arrangement inside the steel-concrete joint section can be constructed by the transverse slice image and the longitudinal slice image, and the displacement of the steel bar in the transverse direction and the longitudinal direction is obtained, and the motion of the steel bar is judged by combining them.

[0061] Optionally, in some embodiments, the interface slip of all the steel bars inside can not be monitored, and the steel bars at the edge position of the steel-concrete joint section are preferably monitored, for example, the nearest steel bar fitting point to the electromagnetic wave emitting device in the horizontal slice two-dimensional image is selected, and the coordinate change of the fitting point is monitored.

[0062] Calculate steel bar deformation: calculate the distance between the fitting points of adjacent steel bars in the same steel bar reflection image;

[0063] According to the change of the distance between the fitting points of adjacent steel bars in the adjacent steel bar reflection image, the steel bar deformation information is obtained, and the adjacent steel bar reflection image is the steel bar reflection image adjacent in time. Thus the change of the distance between the steel bars with time can be obtained, so as to judge whether the steel bar is deformed; at the same time, according to the three-dimensional structure of the steel bar arrangement, it can be judged in which direction the steel bar itself is deformed.

[0064] S400, edge extraction is performed on the steel-concrete contact surface image, and a maximum inscribed circle is drawn between the extracted steel plate edge and the concrete edge, whether there is interface separation between the steel plate and the concrete is judged according to the size of the maximum inscribed circle, and interface information is obtained; comprising:

[0065] S410, edge extraction and curve fitting are performed on the steel-concrete contact surface image to obtain two curves;

[0066] S420, a Voronoi diagram is generated based on the two curves, and a sequence of symmetric points of the two curves is obtained according to the Voronoi diagram;

[0067] The Voronoi diagram is composed of a group of continuous polygons composed of perpendicular bisectors connecting two adjacent points. Specifically, uniformly sample points on the curve as generating points, the edges of the generated Voronoi diagram are regarded as the symmetry axes between the curves, and the intersection points of the symmetry axes and the lines connecting the two generating points are found to obtain the symmetry points, and a sequence of symmetry points is constructed.

[0068] S430, find the point farthest from the two curves in the sequence of symmetry points to obtain the center of the maximum inscribed circle;

[0069] S440, according to the distance of the center of the maximum inscribed circle to the two curves, judge whether there is an interface separation between the steel plate and the concrete to obtain the interface information. Specifically, the steel-concrete contact surface can be tested in advance to obtain the diameter size of the maximum inscribed circle under the condition of no interface separation, and a 10% tolerance is added to the size to set a separation threshold. When the diameter of the maximum inscribed circle is greater than the separation threshold, it is judged that there is an interface separation between the steel plate and the concrete.

[0070] S500, construct a three-dimensional model of the void area based on the void image, and obtain the volume information of the void area;

[0071] Identify the void area in the void image of the transverse slice image and the longitudinal slice image; count the pixels of the void area, and calculate the void area of each void area in the slice based on the pixel calibration coefficient. The pixel calibration coefficient is used to convert the pixel size to the actual size.

[0072] By calculating the overlapping coordinates of the void areas of adjacent transverse slice images, the void areas are stacked to obtain a first overlapping area; adjacent transverse slice images are two slice images that overlap in space, and the overlapping coordinates are aligned. A number of continuous adjacent slices can construct a three-dimensional structure of the void area by stacking. The first overlapping area is the overlapping area perpendicular to the image plane direction.

[0073] By calculating the overlapping coordinates of the void areas of adjacent longitudinal slice images, the void areas are stacked to obtain a second overlapping area;

[0074] Construct a three-dimensional model of the void area based on the first overlapping area and the second overlapping area, and calculate the volume information of the void area based on the three-dimensional model of the void area.

[0075] The stacking direction of the first overlapping region and the second overlapping region is perpendicular to each other, and the stacking depth of the first overlapping region is the length direction of the second overlapping region, and vice versa. Inside the steel-concrete joint section, there can be multiple independent void areas; for example, there are two void areas, and the coordinates of the two void areas on the transverse slice image overlap, but it cannot be determined whether the two void areas are connected in space only by the transverse slice image. At this time, longitudinal slice image analysis is needed, according to the depth of the two void areas, find their coordinates in the longitudinal slice image, see if the two coordinates are connected in the longitudinal slice image, if so, the two void areas are connected.

[0076] This step realizes the three-dimensional space segmentation of the void area through the above-mentioned bidirectional coincidence coordinate calculation, and can obtain multiple independent three-dimensional void areas and construct a three-dimensional model. The three-dimensional volume of the void area is calculated according to the spatial intersection coordinates of the transverse slice image and the longitudinal slice image of the segmented void area edge, and the actual underground position of the void area can be obtained through the coordinates.

[0077] S600, based on the interface information, steel bar slip and deformation information and void area volume information, obtaining the disease information of the steel-concrete joint section;

[0078] The above information is collected at a certain frequency for continuous monitoring. The slice data collected each time is detected, calculated and identified according to the above-mentioned process. When interface separation, steel bar slip or deformation, or void is identified, a warning is given. At the same time, data accumulation can be performed in the time dimension. The steel-concrete joint interface slip amount, the steel bar relative displacement amount, the steel plate concrete interface separation amount and the void volume in the time dimension are obtained, and the disease information is summarized and obtained. Through time series analysis, the service performance is evaluated, the service life of the structure is predicted, the maintenance decision is made based on the disease information, and the maintenance practice of the steel-concrete joint section is guided.

[0079] Embodiment 2

[0080] The application also provides a steel-concrete joint section disease monitoring device, comprising:

[0081] The first construction module 100 is used for acquiring reflection signal data of the steel-concrete joint section through electromagnetic detection, and constructing a slice two-dimensional image of the internal structure of the steel-concrete joint section according to the reflection signal data;

[0082] The first segmentation module 200 is used for target recognition and segmentation of the slice two-dimensional image by using a pre-trained model, to obtain a monitoring target image, wherein the monitoring target image includes a steel bar reflection image, a steel-concrete contact surface image and a void image;

[0083] The first processing module 300 is configured to perform elliptical fitting on the steel bar reflection image to obtain fitting points of each steel bar, and obtain steel bar slip and deformation information according to position changes of the fitting points of the steel bars.

[0084] The second processing module 400 is configured to perform edge extraction on the steel-concrete contact surface image, draw a maximum inscribed circle between the extracted steel plate edge and the concrete edge, and determine whether an interface separation exists between the steel plate and the concrete according to a size of the maximum inscribed circle to obtain interface information.

[0085] The third processing module 500 is configured to construct a three-dimensional model of the void area based on the void image, and obtain void area volume information.

[0086] The fourth processing module 600 is configured to obtain steel-concrete joint segment disease information based on the displacement monitoring information, the steel bar slip and deformation information, and the void area volume information.

[0087] As an optional implementation, the first processing module 300 includes:

[0088] The fitting unit 310 is configured to perform elliptical fitting on the steel bar reflection image, and obtain vertex coordinates of each ellipse, and take the vertex coordinates as fitting points of each steel bar.

[0089] The first calculation unit 320 is configured to number the fitting points of each steel bar according to the vertex coordinates, and calculate coordinate change values of fitting points of the same numbered steel bar in adjacent steel bar reflection images; the adjacent steel bar reflection images are steel bar reflection images adjacent in time.

[0090] The first processing unit 330 is configured to obtain steel bar slip information according to the coordinate change values of the fitting points of the steel bars.

[0091] The second calculation unit 340 is configured to calculate distances between fitting points of adjacent steel bars in the same steel bar reflection image.

[0092] The second processing unit 350 is configured to obtain steel bar deformation information according to changes in the distances between the fitting points of the adjacent steel bars in the adjacent steel bar reflection images.

[0093] As an optional implementation, the second processing module 400 includes:

[0094] The extraction unit 410 is configured to perform edge extraction on the steel-concrete contact surface image to obtain two curves.

[0095] The generation unit 420 is configured to generate a Voronoi diagram based on the two curves, and obtain a sequence of symmetric points of the two curves according to the Voronoi diagram.

[0096] The searching unit 430 is configured to search for a point farthest from the two curves in the symmetry point sequence to obtain a maximum inscribed circle center;

[0097] The judging unit 440 is configured to judge whether there is an interface separation between the steel plate and the concrete according to the distance of the maximum inscribed circle center to the two curves to obtain interface information.

[0098] As an optional implementation, the slice two-dimensional image includes a transverse slice image and a longitudinal slice image; and the third processing module 500 includes:

[0099] The identifying unit 510 is configured to identify a void area in a void image of the transverse slice image and the longitudinal slice image;

[0100] The third calculating unit 520 is configured to perform void area stacking by calculating overlapping coordinates of the void areas of adjacent transverse slice images to obtain a first overlapping area;

[0101] The fourth calculating unit 530 is configured to perform void area stacking by calculating overlapping coordinates of the void areas of adjacent longitudinal slice images to obtain a second overlapping area;

[0102] The fifth calculating unit 540 is configured to construct a three-dimensional model of the void area based on the first overlapping area and the second overlapping area, and calculate void area volume information according to the three-dimensional model of the void area.

[0103] Embodiment 3

[0104] Corresponding to the above method embodiment, the present embodiment also provides a steel-concrete joint segment disease monitoring device. The steel-concrete joint segment disease monitoring device described below can be mutually corresponding and referred to with the steel-concrete joint segment disease monitoring method described above.

[0105] Figure 3 is a block diagram of a steel-concrete joint segment disease monitoring device 800 according to an exemplary embodiment. As shown in Figure 3As shown, the steel-concrete joint disease monitoring device 800 includes a processor 801 and a memory 802. The steel-concrete joint disease monitoring device 800 can also include one or more of a multimedia component 803, an input / output (I / O) interface 804, and a communication component 805. The processor 801 is configured to control the overall operation of the steel-concrete joint disease monitoring device 800 to accomplish all or part of the steps of the steel-concrete joint disease monitoring method described above. The memory 802 is configured to store various types of data to support the operation of the steel-concrete joint disease monitoring device 800, such as commands for any application or method operating on the steel-concrete joint disease monitoring device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, and the like. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0106] The multimedia component 803 can include a screen, such as a touch screen, and an audio component for outputting and / or inputting audio signals. For example, the audio component can include a microphone for receiving external audio signals.

[0107] The received audio signals can be further stored in the memory 802 or transmitted through the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, which can be a keyboard, a mouse, a button, and the like. These buttons can be virtual buttons or physical buttons. The communication component 805 is configured to enable wired or wireless communication between the steel-concrete joint disease monitoring device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include a Wi-Fi module, a Bluetooth module, and an NFC module.

[0108] In an example embodiment, the device 800 for digital file mutual signing and mutual verification can be implemented by one or more Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor or other electronic elements for performing the above-mentioned steel-concrete joint disease monitoring method.

[0109] In another example embodiment, a computer readable storage medium including program commands is also provided, which, when executed by a processor, implements the steps of the above-mentioned steel-concrete joint disease monitoring method. For example, the computer readable storage medium can be the above-mentioned memory 802 including program commands, and the above-mentioned program commands can be executed by the processor 801 of the steel-concrete joint disease monitoring device 800 to complete the above-mentioned steel-concrete joint disease monitoring method.

[0110] Embodiment 4

[0111] Corresponding to the above-mentioned steel-concrete joint disease monitoring method embodiment, a readable storage medium is also provided in this embodiment, and the readable storage medium described below can be referred to in correspondence with the above-mentioned steel-concrete joint disease monitoring method.

[0112] A readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the above-mentioned steel-concrete joint disease monitoring method embodiment.

[0113] The readable storage medium can be specifically a U disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various readable storage media that can store program codes.

[0114] It should be noted that, in the present document, relational terms such as "first" and "second", and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0115] The above description is only specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for monitoring defects in steel-concrete composite sections, characterized in that, include Electromagnetic detection is used to obtain reflected signal data of the steel-concrete composite section, and a slice two-dimensional image of the internal structure of the steel-concrete composite section is constructed based on the reflected signal data. A pre-trained model is used to perform target recognition and segmentation on the sliced ​​two-dimensional image to obtain a monitoring target image, which includes a steel bar reflection image, a steel-concrete contact surface image, and a void image; Ellipse fitting is performed on the reflection image of the steel bars to obtain the fitting points of each steel bar. Based on the positional changes of the fitting points of the steel bars, the slippage and deformation information of the steel bars are obtained. Edge extraction is performed on the steel-concrete contact surface image, and the largest inscribed circle is drawn between the extracted steel plate edge and concrete edge. The size of the largest inscribed circle is used to determine whether there is interface separation between the steel plate and the concrete, and interface information is obtained. A three-dimensional model of the vacuolated region is constructed based on the vacuolated image, and the volume information of the vacuolated region is obtained; Based on the interface information, rebar slippage and deformation information, and void area volume information, the defect information of the steel-concrete composite section is obtained.

2. The method for monitoring defects in steel-concrete composite sections according to claim 1, characterized in that, Ellipse fitting is performed on the reflection image of the reinforcing bars to obtain the fitting points for each reinforcing bar. Based on the positional changes of the fitting points, the slippage and deformation information of the reinforcing bars is obtained, including: Ellipse fitting is performed on the reflection image of the steel bars, and the vertex coordinates of each ellipse are obtained. The vertex coordinates are used as the fitting points for each steel bar. Based on the vertex coordinates, the fitting points of each rebar are numbered, and the coordinate change values ​​of the fitting points of the same numbered rebar in the reflection images of adjacent rebars are calculated; the adjacent rebar reflection images are rebar reflection images acquired at adjacent times. Based on the coordinate changes of the fitted points of the reinforcing bars, the slippage information of the reinforcing bars is obtained; Calculate the spacing between the fitted points of adjacent steel bars in the same steel bar reflection image; The deformation information of the steel bars is obtained by measuring the change in the spacing between the fitting points of adjacent steel bars in the reflection images of adjacent steel bars.

3. The method for monitoring defects in steel-concrete composite sections according to claim 1, characterized in that, Edge extraction is performed on the image of the steel-concrete interface. A maximum inscribed circle is drawn between the edges of the steel plate and the concrete. Based on the maximum inscribed circle, it is directly determined whether there is interface separation between the steel plate and the concrete to obtain interface information, including: Edge extraction was performed on the image of the steel-concrete contact surface to obtain two curves; A Volonoi diagram is generated based on the two curves, and a sequence of symmetrical points of the two curves is obtained from the Volonoi diagram. Find the point farthest from the two curves in the symmetrical point sequence to obtain the center of the largest inscribed circle; Based on the distance from the center of the largest inscribed circle to the two curves, it is determined whether there is interface separation between the steel plate and the concrete, and interface information is obtained.

4. The method for monitoring defects in steel-concrete composite sections according to claim 1, characterized in that, The sliced ​​two-dimensional image includes horizontal slice images and vertical slice images; a three-dimensional model of the voided region is constructed based on the voided image, and the volume information of the voided region is obtained, including: Identify the voided regions in the voided images of the horizontal slice image and the vertical slice image; The first overlapping region is obtained by stacking the empty regions of adjacent horizontal slice images by calculating the coincidence coordinates of the empty regions. By calculating the coincidence coordinates of the hollowed-out regions of adjacent vertical slice images, the hollowed-out regions are stacked to obtain the second overlapping region; A three-dimensional model of the voided region is constructed based on the first and second overlapping regions, and the volume information of the voided region is calculated based on the three-dimensional model of the voided region.

5. A device for monitoring defects in steel-concrete composite sections, characterized in that, include: The first construction module is used to acquire reflected signal data of the steel-concrete composite section through electromagnetic detection, and to construct a sliced ​​two-dimensional image of the internal structure of the steel-concrete composite section based on the reflected signal data. The first segmentation module is used to perform target recognition and segmentation on the sliced ​​two-dimensional image using a pre-trained model to obtain a monitoring target image, which includes a steel bar reflection image, a steel-concrete contact surface image, and a void image. The first processing module is used to perform ellipse fitting on the reflection image of the steel bar to obtain the fitting point of each steel bar, and to obtain the slippage and deformation information of the steel bar based on the position change of the fitting point of the steel bar. The second processing module is used to extract the edges of the steel-concrete contact surface image, draw the maximum inscribed circle between the extracted steel plate edge and the concrete edge, and determine whether there is interface separation between the steel plate and the concrete based on the size of the maximum inscribed circle to obtain interface information. The third processing module is used to construct a three-dimensional model of the voided region based on the voided image and obtain the volume information of the voided region; The fourth processing module is used to obtain the defect information of the steel-concrete composite section based on the interface information, the steel bar slippage and deformation information and the void area volume information.

6. The steel-concrete composite section defect monitoring device according to claim 5, characterized in that, The first processing module includes: The fitting unit is used to perform ellipse fitting on the reflection image of the steel bar and obtain the vertex coordinates of each ellipse, and use the vertex coordinates as the fitting point of each steel bar. The first calculation unit is used to number the fitting points of each rebar according to the vertex coordinates, and calculate the coordinate change value of the fitting points of the same numbered rebar in the reflection images of adjacent rebars; the adjacent rebar reflection images are rebar reflection images acquired in adjacent time periods. The first processing unit is used to obtain the slip information of the reinforcing bar based on the coordinate change value of the fitted point of the reinforcing bar; The second calculation unit is used to calculate the spacing between the fitting points of adjacent steel bars in the same steel bar reflection image; The second processing unit is used to obtain steel bar deformation information based on the change in the spacing between the fitting points of adjacent steel bars in the reflection images of adjacent steel bars.

7. The steel-concrete composite section defect monitoring device according to claim 5, characterized in that, The second processing module includes: The extraction unit is used to extract the edges of the steel-concrete contact surface image to obtain two curves; The generation unit is used to generate a Volonoi diagram based on the two curves, and to obtain a sequence of symmetrical points of the two curves based on the Volonoi diagram; The search unit is used to find the point farthest from the two curves in the sequence of symmetrical points, and to obtain the center of the largest inscribed circle. The judgment unit is used to determine whether there is interface separation between the steel plate and the concrete based on the distance from the center of the largest inscribed circle to the two curves, and to obtain interface information.

8. The steel-concrete composite section defect monitoring device according to claim 5, characterized in that, The sliced ​​two-dimensional image includes horizontal slice images and vertical slice images; the third processing module includes: The identification unit is used to identify the hollowed-out regions in the hollowed-out images of the horizontal slice image and the vertical slice image; The third calculation unit is used to stack the empty regions by calculating the coincident coordinates of the empty regions of adjacent horizontal slice images to obtain the first overlapping region; The fourth calculation unit is used to stack the empty regions by calculating the coincident coordinates of the empty regions of adjacent vertical slice images to obtain the second overlapping region; The fifth calculation unit is used to construct a three-dimensional model of the voided region based on the first overlapping region and the second overlapping region, and to calculate the volume information of the voided region based on the three-dimensional model of the voided region.

9. A monitoring device for defects in steel-concrete composite sections, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the steel-concrete composite section defect monitoring method as described in any one of claims 1 to 4.

10. A readable storage medium, characterized in that, The readable storage medium stores a computer program that, when executed by a processor, implements the steps of the steel-concrete composite section defect monitoring method as described in any one of claims 1 to 4.

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